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Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future
arXiv - CS - Computers and Society Pub Date : 2021-06-08 , DOI: arxiv-2106.04420
Harshavardhan Kamarthi, Alexander Rodríguez, B. Aditya Prakash

In real-time forecasting in public health, data collection is a non-trivial and demanding task. Often after initially released, it undergoes several revisions later (maybe due to human or technical constraints) - as a result, it may take weeks until the data reaches to a stable value. This so-called 'backfill' phenomenon and its effect on model performance has been barely studied in the prior literature. In this paper, we introduce the multi-variate backfill problem using COVID-19 as the motivating example. We construct a detailed dataset composed of relevant signals over the past year of the pandemic. We then systematically characterize several patterns in backfill dynamics and leverage our observations for formulating a novel problem and neural framework Back2Future that aims to refines a given model's predictions in real-time. Our extensive experiments demonstrate that our method refines the performance of top models for COVID-19 forecasting, in contrast to non-trivial baselines, yielding 18% improvement over baselines, enabling us obtain a new SOTA performance. In addition, we show that our model improves model evaluation too; hence policy-makers can better understand the true accuracy of forecasting models in real-time.

中文翻译:

Back2Future:利用回填动态改进未来的实时预测

在公共卫生的实时预测中,数据收集是一项艰巨而艰巨的任务。通常在最初发布后,它会在之后进行多次修订(可能由于人为或技术限制)——因此,数据达到稳定值可能需要数周时间。这种所谓的“回填”现象及其对模型性能的影响在先前的文献中几乎没有研究过。在本文中,我们以 COVID-19 为例介绍了多变量回填问题。我们构建了一个由过去一年大流行的相关信号组成的详细数据集。然后,我们系统地描述了回填动态中的几种模式,并利用我们的观察来制定一个新的问题和神经框架 Back2Future,旨在实时改进给定模型的预测。我们的大量实验表明,与非平凡基线相比,我们的方法改进了 COVID-19 预测顶级模型的性能,比基线提高了 18%,使我们能够获得新的 SOTA 性能。此外,我们表明我们的模型也改进了模型评估;因此,决策者可以更好地实时了解预测模型的真实准确性。
更新日期:2021-06-09
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